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Learning to Learn in a Semi-supervised Fashion

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12363)

Abstract

To address semi-supervised learning from both labeled and unlabeled data, we present a novel meta-learning scheme. We particularly consider that labeled and unlabeled data share disjoint ground truth label sets, which can be seen tasks like in person re-identification or image retrieval. Our learning scheme exploits the idea of leveraging information from labeled to unlabeled data. Instead of fitting the associated class-wise similarity scores as most meta-learning algorithms do, we propose to derive semantics-oriented similarity representations from labeled data, and transfer such representation to unlabeled ones. Thus, our strategy can be viewed as a self-supervised learning scheme, which can be applied to fully supervised learning tasks for improved performance. Our experiments on various tasks and settings confirm the effectiveness of our proposed approach and its superiority over the state-of-the-art methods.

Notes

Acknowledgments

This paper is supported in part by the Ministry of Science and Technology (MOST) of Taiwan under grant MOST 109-2634-F-002-037.

Supplementary material

504473_1_En_27_MOESM1_ESM.pdf (275 kb)
Supplementary material 1 (pdf 274 KB)

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Authors and Affiliations

  1. 1.Graduate Institute of Communication EngineeringNational Taiwan UniversityTaipeiTaiwan
  2. 2.ASUS Intelligent Cloud ServicesTaipeiTaiwan

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